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Paper Abstract and Keywords
Presentation 2025-12-04 13:45
Interval Estimation of Software Reliability Using Evidence Neural Networks
Ishin Kimura, Xiao Xiao (TMU) R2025-49
Abstract (in Japanese) (See Japanese page) 
(in English) To ensure the quality of software systems, quantitative evaluation of reliability is essential. While deep learning-based failure prediction has been applied in recent years, most approaches remain limited to point predictions, lacking the ability to assess prediction uncertainty. This paper proposes a novel interval estimation method for software reliability using Evidence Neural Networks (ENN). While ENNs have been applied to tasks like predicting capacity degradation in lithium-ion batteries, this research extends the approach to suit the characteristics of software failure data. A key feature of the proposed method is its adaptability to predict for any number of detected failures, independent of the specific data type. Experiments using actual software failure data demonstrate the effectiveness of this method and explore its applicability to software reliability prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Software Reliability / Evidence Neural Networks / Interval Estimation / Uncertainty Quantification / Fault Data Analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 268, R2025-49, pp. 1-6, Dec. 2025.
Paper # R2025-49 
Date of Issue 2025-11-27 (R) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF R2025-49

Conference Information
Committee R  
Conference Date 2025-12-04 - 2025-12-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reliability International Standard, Maintainability, Reliability General, Safety General 
Paper Information
Registration To R 
Conference Code 2025-12-R 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Interval Estimation of Software Reliability Using Evidence Neural Networks 
Sub Title (in English)  
Keyword(1) Software Reliability  
Keyword(2) Evidence Neural Networks  
Keyword(3) Interval Estimation  
Keyword(4) Uncertainty Quantification  
Keyword(5) Fault Data Analysis  
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1st Author's Name Ishin Kimura  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Xiao Xiao  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2025-12-04 13:45:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2025-49 
Volume (vol) vol.125 
Number (no) no.268 
Page pp.1-6 
#Pages 6 
Date of Issue 2025-11-27 (R) 

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